Lumbar intervertebral disc segmentation for computer modeling and simulation.

BACKGROUND AND OBJECTIVE: The present work had as its main objective the development of a method for localizing and automatically segmenting lumbar intervertebral discs (IVD) in 3D from magnetic resonance imaging (MRI), with the goal of supporting the generation of finite element (FE) models from actual lumbar spine anatomy, by providing accurate and personalized information on the shape of the patient's IVD. The extension of the method to allow performing separate segmentations of the IVD's two main structures - annulus fibrosus (AF) and nucleus pulposus (NP) - as well as automatically detecting degenerated IVD where this distinction is no longer possible was also an objective of the work. METHODS: The method presented here evolves from 2D segmentations in the sagittal profile using Gabor filters towards 3D segmentations. It works by detecting the spine curves and intensity regions corresponding to IVD. As so, the 2D method from Zhu et al. (2013) was partially implemented, modified and adapted to 3D use, and then tested with eight spines from two separated online datasets. The 3D adaptation was achieved by using vertebral body segmentation masks to approximate the shape of the vertebrae and to adjust the spine curves accordingly. RESULTS: The method showed average values of 85%, 83% and 96% for the Dice coefficient, sensitivity and specificity, respectively. The method correctly identified 65 of 68 (96%) IVD as either healthy or degenerated. The method's Dice coefficient is within the range of existing 3D IVD segmentation methods in the literature (81-92%). The method took on average 6-7 s to perform a full 3D segmentation, which is well within the range of the existing methods (2 s - 19 min). CONCLUSIONS: The developed method can be used to generate accurate 3D models of the IVD based on MRI, with AF/NP distinction and detection of marked degeneration by comparing each IVD with the remaining spine levels. Further work shall improve the method towards distinguishing between specific levels of degeneration for clinically oriented FE modeling.

Impact of total variation minimization in volume rendering visualization of breast tomosynthesis data.

BACKGROUND AND OBJECTIVE: Total Variation (TV) minimization algorithms have achieved great attention due to the virtue of decreasing noise while preserving edges. The purpose of this work is to implement and evaluate two TV minimization methods in 3D. Their performance is analyzed through 3D visualization of digital breast tomosynthesis (DBT) data with volume rendering. METHODS: Both filters were studied with real phantom and one clinical DBT data. One algorithm was applied sequentially to all slices and the other was applied to the entire volume at once. The suitable Lagrange multiplier used in each filter equation was studied to reach the minimum 3D TV and the maximum contrast-to-noise ratio (CNR). Imaging blur was measured at 0° and 90° using two disks with different diameters (0.5 mm and 5.0 mm) and equal thickness. The quality of unfiltered and filtered data was analyzed with volume rendering at 0° and 90°. RESULTS: For phantom data, with the sequential filter, a decrease of 25% in 3D TV value and an increase of 19% and 30% in CNR at 0° and 90°, respectively, were observed. When the filter is applied directly in 3D, TV value was reduced by 35% and an increase of 36% was achieved both for CNR at 0° and 90°. For the smaller disk, variations of 0% in width at half maximum (FWHM) at 0° and a decrease of about 2.5% for FWHM at 90° were observed for both filters. For the larger disk, there was a 2.5% increase in FWHM at 0° for both filters and a decrease of 6.28% and 1.69% in FWHM at 90° with the sequential filter and the 3D filter, respectively. When applied to clinical data, the performance of each filter was consistent with that obtained with the phantom. CONCLUSIONS: Data analysis confirmed the relevance of these methods in improving quality of DBT images. Additionally, this type of 3D visualization showed that it may play an important complementary role in DBT imaging. It allows to visualize all DBT data at once and to analyze properly filters applied to all the three dimensions. Concise Abstract Total Variation (TV) minimization algorithms are one compressed sensing technique that has achieved great attention due to the virtue of decrease noise while preserve edges transitions. The purpose of this work is to solve the same TV minimization problem in DBT data, by studying two 3D filters. The obtained results were analyzed at 0° and 90° with a 3D visualization through volume rendering. The filters differ in their application. One considers a slice-by-slice optimization, sequentially traversing all slices of the data. The other considers the intensity values of adjacent slices to make this optimization on each voxel. The performance of each filter was also tested with a clinical case. The results obtained were very encouraging with a significantly increased contrast to noise ratio at 0° and 90° and a small reduction in blur at 90° (slight reduction of the out-of-plane artifact).

Activity estimation and biokinetic analysis of 99mTc-DMSA in renal infant patients using a gamma camera.

Biokinetic data from the administration of radiopharmaceuticals is essential in nuclear medicine dosimetry. It has particular significance in children, as their metabolism is very different from adults. Biokinetic models for paediatric patients could therefore need to be adapted to better reflect their absorption, retention and excretion functions, when compared to adults. Obtaining quality in vivo infant or paediatric biokinetic data is then essential to improve the available reference models, which in turn can lead to the optimization of paediatric procedures and protocols in clinical practice. This study analyses the biokinetic behaviour of 99mTc-dimercaptosuccinic acid (DMSA), in 8 infants aged 4 months to 2 years old, through an imaging study using a gamma camera, and compares the obtained values with those obtained with the reference ICRP biokinetic model. The in vivo data was treated using an adapted methodology from the MIRD 16 pamphlet. Activity curves for the liver, the kidney and the whole body, were built, and new effective absorption, retention and excretion half-lives were estimated, and compared with the reference biokinetic parameters of ICRP 128. The obtained residence time in the kidneys of 2.56 h, has a deviation of 30.8% to the ICRP 128 value of 3.70 h. The obtained maximum uptake in the kidneys was of 0.22/A0, which compares to the value of 0.31/A0 for ICRP. The obtained biokinetic parameters were used to estimate the absorbed dose. The obtained dose values are smaller than the reference ICRP 128 ones by 32.1% in the kidneys, and 18.4% in the liver.

Dynamic relaxation in algebraic reconstruction technique (ART) for breast tomosynthesis imaging.

BACKGROUND AND OBJECTIVES: A major challenge in Digital Breast Tomosynthesis (DBT) is handling image noise since the 3D reconstructed images are obtained from low dose projections and limited angular range. The use of the iterative reconstruction algorithm Algebraic Reconstruction Technique (ART) in clinical context depends on two key factors: the number of iterations needed (time consuming) and the image noise after iterations. Both factors depend highly on a relaxation coefficient (λ), which may give rise to slow or noisy reconstructions, when a single λ value is considered for the entire iterative process. The aim of this work is to present a new implementation for the ART that takes into account a dynamic mode to calculate λ in DBT image reconstruction. METHODS: A set of initial reconstructions of real phantom data was done using constant λ values. The results were used to choose, for each iteration, the suitable λ value, taking into account the image noise level and the convergence speed. A methodology to optimize λ automatically during the image reconstruction was proposed. RESULTS: Results showed we can dynamically choose λ values in such a way that the time needed to reconstruct the images can be significantly reduced (up to 70%) while achieving similar image quality. These results were confirmed with one clinical dataset. CONCLUSIONS: With simple methodology we were able to dynamically choose λ in DBT image reconstruction with ART, allowing a shorter image reconstruction time without increasing image noise.

Effect of the glandular composition on digital breast tomosynthesis image quality and dose optimisation.

In the image quality assessment for digital breast tomosynthesis (DBT), a breast phantom with an average percentage of 50 % glandular tissue is seldom used, which may not be representative of the breast tissue composition of the women undergoing such examination. This work aims at studying the effect of the glandular composition of the breast on the image quality taking into consideration different sizes of lesions. Monte Carlo simulations were performed using the state-of-the-art computer program PENELOPE to validate the image acquisition system of the DBT equipment as well as to calculate the mean glandular dose for each projection image and for different breast compositions. The integrated PENELOPE imaging tool (PenEasy) was used to calculate, in mammography, for each clinical detection task the X-ray energy that maximises the figure of merit. All the 2D cranial-caudal projections for DBT were simulated and then underwent the reconstruction process applying the Simultaneous Algebraic Reconstruction Technique. Finally, through signal-to-noise ratio analysis, the image quality in DBT was assessed.

Optimal photon energy comparison between digital breast tomosynthesis and mammography: a case study.

A comparison, in terms of the optimal energy that maximizes the image quality between digital breast tomosynthesis (DBT) and digital mammography (DM) was performed in a MAMMOMAT Inspiration system (Siemens) based on amorphous selenium flat panel detector. In this paper we measured the image quality by the signal difference-to-noise ratio (SDNR), and the patient risk by the mean glandular dose (MGD). Using these quantities we compared the optimal voltage that maximizes the image quality both in breast tomosynthesis and standard mammography acquisition mode. The comparison for the two acquisition modes was performed for a W/Rh anode filter combinations by using a 4.5 cm tissue equivalent mammography phantom. Moreover, in order to check if the used equipment was quantum noise limited, the relation of the relative noise with respect to the detector dose was evaluated. Results showed that in the tomosynthesis acquisition mode the optimal voltage is 28 kV, whereas in standard mammography the optimal voltage is 30 kV. The automatic exposure control (AEC) of the system selects 28 kV as optimal voltage both for DBT and DM. Monte Carlo simulations showed a qualitative agreement with the AEC selection system, since an optimal monochromatic energy of 20 keV was found both for DBT and DM. Moreover, the check about the noise showed that the system is not completely quantum noise limited, and this issue could explain the experimental slight difference in terms of optimal voltage between DBT and DM. According to these results, the use of higher voltage settings is not justified for the improvement of the image quality during a DBT examination.

Choosing the ART relaxation parameter for Clear-PEM 2D image reconstruction.

The Algebraic Reconstruction Technique (ART) is an iterative image reconstruction algorithm. During the development of the Clear-PEM device, a PET scanner designed for the evaluation of breast cancer, multiple tests were done in order to optimise the reconstruction process. The comparison between ART, MLEM and OSEM indicates that ART can perform faster and with better image quality than the other, most common algorithms. It is claimed in this paper that if ART's relaxation parameter is carefully adjusted to the reconstruction procedure it can produce high quality images in short computational time. This is confirmed by showing that with the relaxation parameter evolving as a logarithmic function, ART can match in terms of image quality and overcome in terms of computational time the performance of MLEM and OSEM algorithms. However, this study was performed only with simulated data and the level of noise with real data may be different.

Optimization of 2D image reconstruction for positron emission mammography using IDL.

The Clear-PEM system is a prototype machine for Positron Emission Mammography (PEM) under development within the Portuguese PET-Mammography consortium. We have embedded 2D image reconstruction algorithms implemented in IDL within the prototype's image analysis package. The IDL implementation of these algorithms proved to be accurate and computationally efficient. In this paper, we present the implementation of the MLEM, OSEM and ART 2D iterative image reconstruction algorithms for PEM using IDL. C and IDL implementations are compared using realistic Monte Carlo simulated data. We show that IDL can be used for the easy implementation of image reconstruction algorithms for emission tomography.

Clear-PEM: a dedicated PET camera for improved breast cancer detection.

Positron emission mammography (PEM) can offer a non-invasive method for the diagnosis of breast cancer. Metabolic images from PEM using 18F-fluoro-deoxy-glucose, contain unique information not available from conventional morphologic imaging techniques like X-ray radiography. In this work, the concept of Clear-PEM, the system presently developed in the frame of the Crystal Clear Collaboration at CERN, is described. Clear-PEM will be a dedicated scanner, offering better perspectives in terms of position resolution and detection sensitivity.